Attendance System

Understudy Attendance System Based On Fingerprint Recognition and One-to-Many Matching A proposal submitted in fractional ful? llment of the prerequisites for the level of Bachelor of Computer Application in Computer Science by Sachin (Roll no. 107cs016) and Arun Sharma (Roll no. 107cs015) Under the direction of : Prof. R. C. Tripathi Department of Computer Science and Engineering National Institute of Technology Rourkela-769 008, Orissa, India 2 . Committed to Our Parents and Indian Scienti? c Community . 3 National Institute of Technology Rourkela Certi? cateThis is to ensure that the task entitled, ‘Student Attendance System Based On Fingerprint Recognition and One-to-Many Matching’ presented by Rishabh Mishra and Prashant Trivedi is a legitimate work done by them under my watch and direction for the fractional ful? llment of the prerequisites for the honor of Bachelor of Technology Degree in Computer Science and Engineering at National Institute of Technology, Rourke la. As far as I could possibly know, the issue typified in the venture has not been submitted to some other University/Institute for the honor of any Degree or Diploma.Date †9/5/2011 Rourkela (Prof. B. Majhi) Dept. of Computer Science and Engineering 4 Abstract Our task targets planning an understudy participation framework which could e? ectively oversee participation of understudies at foundations like NIT Rourkela. Participation is set apart after understudy identi? cation. For understudy identi? cation, a ? ngerprint acknowledgment based identi? cation framework is utilized. Fingerprints are viewed as the best and quickest strategy for biometric identi? cation. They are secure to utilize, one of a kind for each individual and doesn't change in one’s lifetime. Unique mark acknowledgment is an experienced ? ld today, yet at the same time recognizing individual from a lot of selected ? ngerprints is a period taking procedure. It was our obligation to improve the ? ngerp rint identi? cation framework for usage on enormous databases e. g. of a foundation or a nation and so forth. In this task, numerous new calculations have been utilized e. g. sexual orientation estimation, key based one to many coordinating, evacuating limit details. Utilizing these new calculations, we have built up an identi? cation framework which is quicker in usage than some other accessible today in the market. Despite the fact that we are utilizing this ? ngerprint identi? cation framework for understudy identi? ation reason in our task, the coordinating outcomes are acceptable to the point that it could perform very well on huge databases like that of a nation like India (MNIC Project). This framework was executed in Matlab10, Intel Core2Duo processor and correlation of our one to numerous identi? cation was finished with existing identi? cation method I. e. coordinated identi? cation on same stage. Our coordinating procedure runs in O(n+N) time when contrasted with the curr ent O(Nn2 ). The ? ngerprint identi? cation framework was tried on FVC2004 and Veri? nger databases. 5 Acknowledgments We offer our significant thanks and obligation to Prof. B.Majhi, Department of Computer Science and Engineering, NIT, Rourkela for presenting the current point and for their moving scholarly direction, useful analysis and important proposal all through the undertaking work. We are additionally appreciative to Prof. Pankaj Kumar Sa , Ms. Hunny Mehrotra and other sta? s in Department of Computer Science and Engineering for inspiring us in improving the calculations. At last we might want to thank our folks for their help and allowing us remain for additional days to finish this task. Date †9/5/2011 Rourkela Rishabh Mishra Prashant Trivedi Contents 1 Introduction 1. 1. 2 1. 3 1. 4 1. 1. 6 1. 7 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . Inspiration and Challenges . . . . . . . . . . . . . . . . . . . . . . . . Utilizing Biometrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . What is ? ngerprint? . . . . . . . . . . . . . . . . . . . . . . . . . . . Why use ? ngerprints? . . . . . . . . . . . . . . . . . . . . . . . . . . . Utilizing ? ngerprint acknowledgment framework for participation the executives . . . Association of the proposition . . . . . . . . . . . . . . . . . . . . . . . . 17 18 19 21 22 23 24 30 33 35 36 2 Attendance Management Framework 2. 2. 2. 3 2. 4 2. 5 Hardware †Software Level Design . . . . . . . . . . . . . . . . . . . . Participation Management Approach . . . . . . . . . . . . . . . . . . . On-Line Attendance Report Generation . . . . . . . . . . . . . . . . . System and Database Management . . . . . . . . . . . . . . . . . . Utilizing remote system rather than LAN and bringing convenientce . . . 2. 5. 1 2. 6 Using Portable Device . . . . . . . . . . . . . . . . . . . . . . Examination with other understudy participation frameworks . . . . . . . . . . 3 Fingerprint Identi? cation System 3. 1 3. 2 How Fingerprint Recognition functions? . . . . . . . . . . . . . . . . . Unique mark Identi? cation System Flowchart . . . . . . . . . . . . . . 4 Fingerprint Enhancement 4. 1 4. 2 4. 3 Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Standardization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Direction estimation . . . . . . . . . . . . . . . . . . . . . . . . . . 6 CONTENTS 4. 4. 5 4. 6 4. 7 Ridge Frequency Estimation . . . . . . . . . . . . . . . . . . . . . . . Gabor ? lter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Binarisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Diminishing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 38 39 40 41 42 43 44 45 46 47 50 51 53 54 55 56 57 59 60 5 Feature Extraction 5. 1 5. 2 Finding the Reference Point . . . . . . . . . . . . . . . . . . . . . . . Details Extraction and Post-Processing . . . . . . . . . . . . . . . . 5. 2. 1 5. 2. 2 5. 2. 3 5. 3 Minutiae Extraction . . . . . . . . . . . . . . . . . . . . . . . Post-Processing . . . . . . . . . . . . . . . . . . . . . . . . . Evacuating Boundary Minutiae . . . . . . . . . . . . . . . . . . Extraction of the key . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. 3. 1 What is vital? . . . . . . . . . . . . . . . . . . . . . . . . . . Basic Key . . . . . . . . . . . . . . . . . . . . . . . . . . . . Complex Key . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Partitioning of Database 6. 1 6. 2 6. 3 Gender Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . Classi? cation of Fingerprint . . . . . . . . . . . . . . . . . . . . . . . Apportioning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Matching 7. 1 7. 2 7. 3 Alignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Existing Matching Techniques . . . . . . . . . . . . . . . . . . . . . One to Many coordinating . . . . . . . . . . . . . . . . . . . . . . . . . . 7. 3. 1 7. 4 7. 5 Method of One to Many Matching . . . . . . . . . . . . . . . Performing key match and full coordinating . . . . . . . . . . . . . . . . Time Complexity of this coordinating strategy . . . . . . . . . . . . . . 8 Experimental Analysis 8. 1 8. 2 Implementation Environment . . . . . . . . . . . . . . . . . . . . . . Unique mark Enhancement . . . . . . . . . . . . . . . . . . . . . . . . 8. 2. 1 8. 2. 2 Segmentation and Normalization . . . . . . . . . . . . . . . . Direction Estimation . . . . . . . . . . . . . . . . . . . . . . 8. 2. 3 8. 2. 4 8. . 5 8. 3 CONTENTS Ridge Frequency Estimation . . . . . . . . . . . . . . . . . . . Gabor Filters . . . . . . . . . . . . . . . . . . . . . . . . . . . Binarisation and Thinning . . . . . . . . . . . . . . . . . . . . 60 61 62 63 64 65 66 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8. 3. 1 Minutiae Extraction and Post Processing . . . . . . . . . . . . Part iculars Extraction . . . . . . . . . . . . . . . . . . . . . . . In the wake of Removing Spurious and Boundary Minutiae . . . . . . . 8. 3. 2 Reference Point Detection . . . . . . . . . . . . . . . . . . . . 8. 4 Gender Estimation and Classi? ation . . . . . . . . . . . . . . . . . . 8. 4. 1 8. 4. 2 Gender Estimation . . . . . . . . . . . . . . . . . . . . . . . . Classi? cation . . . . . . . . . . . . . . . . . . . . . . . . . . . 8. 5 8. 6 Enrolling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Coordinating . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8. 6. 1 8. 6. 2 Fingerprint Veri? cation Results . . . . . . . . . . . . . . . . . Identi? cation Results and Comparison with Other Matching procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 70 73 74 75 79 8. 7 Performance Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Conclusion 9. 1 Outcomes of this Project . . . . . . . . . . . . . . . . . . . . . . . . . 10 Future Work and Expectations 10. 1 Approach for Future Work A Matlab capacities . . . . . . . . . . . . . . . . . . . . . . . Rundown of Figures 1. 1 2. 1 2. 2. 3 2. 4 2. 5 2. 6 2. 7 2. 8 3. 1 4. 1 4. 2 Example of an edge finishing and a bifurcation . . . . . . . . . . . . . . Equipment present in homerooms . . . . . . . . . . . . . . . . . . . . . Homeroom Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . System Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ER Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Level 0 DFD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Level 1 DFD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Level 2 DFD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Convenient Device . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Unique finger impression Identi? cation System Flowchart . . . . . . . . . . . . . . Direction Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . (a)Original Image, (b)Enhanced Image, (c)Binarised Image, (d)Thinned Image . . . . . . . . . . .